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Summary of the impact

ENABLE is a history matching and uncertainty assessment software system
for the oil industry, whose inference engine was produced by the Durham
Statistics group, based on their research on uncertainty quantification
for complex physical systems modelled by computer simulators. The system
optimizes asset management plans by careful uncertainty quantification and
reduces development costs by accelerating the history matching process for
oil reservoirs, resulting in more informed technical and economic
decision-making. ENABLE was acquired by Roxar ASA in 2006 and current
users include the multinational oil company Statoil. From January 2008 to
September 2012 (the most recent set of figures) the turnover attributed to
ENABLE was [text removed for publication].

Underpinning research

The Durham Statistics Group has a long track record of work on the
quantification of uncertainty for large and complex physical systems
modelled by computer simulators. Much of this work was developed in the
context of history matching for oil reservoirs. This problem may be
described as follows. Reservoir simulators are key tools to help oil
companies manage production for oil reservoirs. The simulator takes as
inputs a description of the reservoir (rock properties, fault distribution
and so on) and returns as outputs the well performance (pressure profiles,
production, water cut and so forth). As the appropriate input choices are
not known, a priori, the input space must be searched to identify choices
of reservoir specification for which the output of the simulator at the
wells corresponds, to an acceptable degree, to recorded historical
behaviour. This process is termed history matching. It is difficult and
challenging because the input space is high dimensional and the evaluation
of the simulator, for a single choice of inputs, takes many hours.

The Durham group devised a detailed Bayesian solution [1] to this
problem, based on building an emulator for the simulator. This is a
probabilistic surrogate for the simulator, giving both a fast
approximation to the simulator and a measure of uncertainty related to the
quality of the approximation. In order to construct the emulator, the
group solved novel problems in prior elicitation, joint Bayesian modelling
for multi-level versions of the simulator, experimental design for
multi-level computer experiments, and diagnostic evaluation for the
resulting construction. This emulator, in combination with an uncertainty
representation for the difference between the simulator and the reservoir,
formed the basis of the history matching methodology that we developed.
This proceeds by eliminating those parts of the input space for which
emulated outputs were too far from observed history, according to a
collection of appropriate implausibility measures, then re-sampling and
re-emulating the simulator within the reduced space, eliminating further
parts of the input space and continuing in this fashion. This is a form of
iterative global search aimed at finding all of the input regions
containing good matches to history.

This work was developed under EPSRC funding, from 1993 to 1995, and was
published in 1997 [1]. The key researchers for the work were
Michael Goldstein, Peter Craig and Allan Seheult, all permanent members of
the Durham Statistics group at that time, and James Smith, PDRA on the
grant from 1993 to 1995.

References to the research

The underpinning work for this research was funded by EPSRC, under the
Complex Stochastic Systems initiative on the grant `Bayes linear
strategies for history matching for hydrocarbon reservoirs' (1993-95,
value £105,000).

One of the outcomes of this research was the invitation to present the
work at the third Case Studies in Bayesian Statistics meetings at Carnegie
Mellon University in October 1995. The Case Study format allowed the
Durham group to make a complete presentation of all of the aspects of its
research. It appeared subsequently as

Only a few invitations to present a case study at this meeting are made,
with the intention of producing very careful and detailed case studies for
a small number of substantial applications. The printed version contains a
discussion by Galway and Lucas, from the RAND Corporation, who refer to
this paper as "superb" and "outstanding". As of 25/10/2013, the paper had
73 citations on google scholar.

Grant applications following from [1] have been very successful,
resulting in support for postdocs on grants from NERC (under the RAPID
programme and the PURE programme), Leverhulme (the Durham Tipping Points
project), EPSRC (The Managing Uncertainty for Complex Models consortium,
funded by the Basic technology initiative) and industry (for example the
Joint Inversion using Bayesian Analysis project, funded by an oil
consortium). (Total value of these grants to the Department: £903,000.)

Details of the impact

As a result of [1], the Durham Statistics group was contracted by
Energy SciTech Ltd (a consultancy firm to the oil industry with which we
have a long research and consultancy connection, and who provided the
reservoir information from which we developed the case study) to write the
inference engine for the system ENABLE which optimizes asset management
planning and reduces costs by accelerating the history matching process
and improving reservoir understanding. Operators now use ENABLE worldwide
for a better understanding and measurement of uncertainty in reservoir
production performance estimates. Using a Bayesian statistical framework
and emulator for the model, based on conventional reservoir simulations,
ENABLE provides companies with a rapid understanding of production
behaviour and the creation of robust uncertainty forecasts.

The contract1 for the software was very precise, in
specifying that we would implement all of the procedures described in the
research case study paper described in section 3. For example,
under Testing and Acceptance of the Software, it was stated that

"The performance of each Module of the Prototype will be deemed
acceptable to both parties if it can be shown that a level of
functionality similar to that demonstrated in Pressure matching for
hydrocarbon reservoirs: a case study in the use of Bayes linear
strategies for large computer experiments (case Studies in Bayesian
Statistics, III, New York: Springer) where consistent with Schedule A, has
been achieved."

Energy Scitech, and thus ENABLE, was acquired by Roxar in 2006. Energy
Scitech existed on sales of ENABLE and services related to ENABLE. When
acquired by Roxar it had revenues of [text removed for publication]
(1 Nov 2004 - 31 Oct 2005) and [text removed for publication] (1
Nov 2005 - 31 Dec 2006)2. Since then, and throughout the
impact period 2008 - 2013, the reach and significance of ENABLE has
continued to grow: in the period 2008 - 2013 active users included [text
removed for publication]3, and the total turnover
attributed to ENABLE sales by Roxar from 1 Jan 2008 to 30 Sept 2012 was [text
removed for publication]4. In the UK there are
currently [text removed for publication] staff working full-time
on ENABLE, and an estimated [text removed for publication] who
spend a proportion of their time on the project3.

Roxar AS is an international provider of products and associated services
for reservoir management and production optimisation in the upstream oil
and gas industry. It is headquartered in Stavanger, Norway and operates in
19 countries with around 900 employees. Roxar offers software for
reservoir interpretation, modelling and simulation, as well as
instrumentation for well planning, monitoring and metering. Roxar was
acquired by Emerson Electric Company in April 2009 and is now part of the
Emerson Process Management Group.

This is how Roxar currently describe the role of ENABLE5:

"History Matching and Uncertainty Quantification. The Roxar ENABLE
solution history matches numerous geological scenarios to create
simulation models that are fully consistent with their underlying
geological interpretation (unlike many current 3D modelling workflows).
RMS, Tempest and ENABLE provide E&P companies with a statistical
framework for a rapid understanding of production behaviour and the
creation of robust estimates from a shared earth model. The result is more
informed technical and economic decision-making and a better
quantification of uncertainty."

This product has been very successful and as specified in the contract1
the University has received a royalty each year to date, based on the
sales of the commercial product. In particular, in the period Jan 2008 -
Sept 2012, [text removed for publication] has been received in
royalties6. Roxar have continued to develop the product,
and to pay royalties to the University, and are committed to ensuring that
it remains current: from June 2011 - May 2012 Roxar had a consultancy
contract for [text removed for publication] with the University to
consider ways to improve the application of ENABLE for complex oil
reservoirs. This was deemed successful and led to a further consultancy
contract being agreed, for [text removed for publication] from
June 2012 to May 20156.

As attested by Roxar7, the software developed in 1998
on the basis of [1] remains a key feature of the Tempest ENABLE
product. The project to integrate ENABLE into Roxar's Tempest suite was
completed in 20128; it brings the Durham developed
emulator and subsequent enhancements to a wider global community.

In 2012, Tempest ENABLE was chosen as the uncertainty platform for the
multinational oil company Statoil9. This has secured
funding for the next three years for [text removed for publication]
software developers in Roxar. The methodology developed in [1],
together with the quality of the associated work delivered by Durham, was
pivotal to the awarding of this contract7.

The Tempest ENABLE product has over [text removed for publication]
active users as of November 20127, and can be regarded
as one of the most successful uncertainty platforms in the oilfield
marketplace, to the point that it has created a new business activity.
Roxar states:7

"The fast Durham developed emulator enables a rigorous statistical
approach to uncertainty quantification. ENABLE was the first commercial
product to allow this in the oil and gas industry. Since ENABLE's release
several competitors have emerged taking advantage of the approach ENABLE
has validated."

The success of ENABLE is confirmed by user feedback. To give one example
from the Tempest ENABLE product website10, Dr
Curt-Albert Schwietzer of GSC Reservoir Simulation & Reserves states:

"After using ENABLE for over one year I can say that reservoir simulation
without ENABLE is unimaginable."